Sample project

Introduction

Akka persistence enables stateful actors to persist their internal state so that it can be recovered when an actor is started, restarted after a JVM crash or by a supervisor, or migrated in a cluster. The key concept behind Akka persistence is that only changes to an actor’s internal state are persisted but never its current state directly (except for optional snapshots). These changes are only ever appended to storage, nothing is ever mutated, which allows for very high transaction rates and efficient replication. Stateful actors are recovered by replaying stored changes to these actors from which they can rebuild internal state. This can be either the full history of changes or starting from a snapshot which can dramatically reduce recovery times. Akka persistence also provides point-to-point communication with at-least-once message delivery semantics.

Note

The General Data Protection Regulation (GDPR) requires that personal information must be deleted at the request of users. Deleting or modifying events that carry personal information would be difficult. Data shredding can be used to forget information instead of deleting or modifying it. This is achieved by encrypting the data with a key for a given data subject id (person) and deleting the key when that data subject is to be forgotten. Lightbend’s GDPR for Akka Persistence provides tools to facilitate in building GDPR capable systems.

Architecture

PersistentActorAbstractPersistentActor: Is a persistent, stateful actor. It is able to persist events to a journal and can react to them in a thread-safe manner. It can be used to implement both command as well as event sourced actors. When a persistent actor is started or restarted, journaled messages are replayed to that actor so that it can recover internal state from these messages.

AtLeastOnceDeliveryAbstractPersistentActorAtLeastOnceDelivery: To send messages with at-least-once delivery semantics to destinations, also in case of sender and receiver JVM crashes.

AsyncWriteJournal: A journal stores the sequence of messages sent to a persistent actor. An application can control which messages are journaled and which are received by the persistent actor without being journaled. Journal maintains highestSequenceNr that is increased on each message. The storage backend of a journal is pluggable. The persistence extension comes with a “leveldb” journal plugin, which writes to the local filesystem. Replicated journals are available as Community plugins.

Snapshot store: A snapshot store persists snapshots of a persistent actor’s internal state. Snapshots are used for optimizing recovery times. The storage backend of a snapshot store is pluggable. The persistence extension comes with a “local” snapshot storage plugin, which writes to the local filesystem. Replicated snapshot stores are available as Community plugins

Event sourcing. Based on the building blocks described above, Akka persistence provides abstractions for the development of event sourced applications (see section Event sourcing).

Event sourcing

A persistent actor receives a (non-persistent) command which is first validated if it can be applied to the current state. Here validation can mean anything, from simple inspection of a command message’s fields up to a conversation with several external services, for example. If validation succeeds, events are generated from the command, representing the effect of the command. These events are then persisted and, after successful persistence, used to change the actor’s state. When the persistent actor needs to be recovered, only the persisted events are replayed of which we know that they can be successfully applied. In other words, events cannot fail when being replayed to a persistent actor, in contrast to commands. Event sourced actors may also process commands that do not change application state such as query commands for example.

Another excellent article about “thinking in Events” is Events As First-Class Citizens by Randy Shoup. It is a short and recommended read if you’re starting developing Events based applications.

Akka persistence supports event sourcing with the PersistentActor traitAbstractPersistentActor abstract class. An actor that extends this traitclass uses the persist method to persist and handle events. The behavior of a PersistentActoran AbstractPersistentActor is defined by implementing receiveRecovercreateReceiveRecover and receiveCommandcreateReceive. This is demonstrated in the following example.

The example defines two data types, Cmd and Evt to represent commands and events, respectively. The state of the ExamplePersistentActor is a list of persisted event data contained in ExampleState.

The persistent actor’s receiveRecovercreateReceiveRecover method defines how state is updated during recovery by handling Evt and SnapshotOffer messages. The persistent actor’s receiveCommandcreateReceive method is a command handler. In this example, a command is handled by generating an event which is then persisted and handled. Events are persisted by calling persist with an event (or a sequence of events) as first argument and an event handler as second argument.

The persist method persists events asynchronously and the event handler is executed for successfully persisted events. Successfully persisted events are internally sent back to the persistent actor as individual messages that trigger event handler executions. An event handler may close over persistent actor state and mutate it. The sender of a persisted event is the sender of the corresponding command. This allows event handlers to reply to the sender of a command (not shown).

The main responsibility of an event handler is changing persistent actor state using event data and notifying others about successful state changes by publishing events.

When persisting events with persist it is guaranteed that the persistent actor will not receive further commands between the persist call and the execution(s) of the associated event handler. This also holds for multiple persist calls in context of a single command. Incoming messages are stashed until the persist is completed.

If persistence of an event fails, onPersistFailure will be invoked (logging the error by default), and the actor will unconditionally be stopped. If persistence of an event is rejected before it is stored, e.g. due to serialization error, onPersistRejected will be invoked (logging a warning by default) and the actor continues with the next message.

It’s also possible to switch between different command handlers during normal processing and recovery with context.become()getContext().become() and context.unbecome()getContext().unbecome(). To get the actor into the same state after recovery you need to take special care to perform the same state transitions with become and unbecome in the receiveRecovercreateReceiveRecover method as you would have done in the command handler. Note that when using become from receiveRecovercreateReceiveRecover it will still only use the receiveRecovercreateReceiveRecover behavior when replaying the events. When replay is completed it will use the new behavior.

Identifiers

A persistent actor must have an identifier that doesn’t change across different actor incarnations. The identifier must be defined with the persistenceId method.

persistenceId must be unique to a given entity in the journal (database table/keyspace). When replaying messages persisted to the journal, you query messages with a persistenceId. So, if two different entities share the same persistenceId, message-replaying behavior is corrupted.

Recovery

By default, a persistent actor is automatically recovered on start and on restart by replaying journaled messages. New messages sent to a persistent actor during recovery do not interfere with replayed messages. They are stashed and received by a persistent actor after recovery phase completes.

The number of concurrent recoveries that can be in progress at the same time is limited to not overload the system and the backend data store. When exceeding the limit the actors will wait until other recoveries have been completed. This is configured by:

akka.persistence.max-concurrent-recoveries = 50

Note

Accessing the sender()sender with getSender() for replayed messages will always result in a deadLetters reference, as the original sender is presumed to be long gone. If you indeed have to notify an actor during recovery in the future, store its ActorPath explicitly in your persisted events.

Recovery customization

Applications may also customise how recovery is performed by returning a customised Recovery object in the recovery method of a PersistentActorAbstractPersistentActor,

To skip loading snapshots and replay all events you can use SnapshotSelectionCriteria.NoneSnapshotSelectionCriteria.none(). This can be useful if snapshot serialization format has changed in an incompatible way. It should typically not be used when events have been deleted.

Another possible recovery customization, which can be useful for debugging, is setting an upper bound on the replay, causing the actor to be replayed only up to a certain point “in the past” (instead of being replayed to its most up to date state). Note that after that it is a bad idea to persist new events because a later recovery will probably be confused by the new events that follow the events that were previously skipped.

Recovery can be disabled by returning Recovery.none() in the recovery method of a PersistentActor:

Scala

override def recovery = Recovery.none

Java

@Override
public Recovery recovery() {
return Recovery.none();
}

Recovery status

A persistent actor can query its own recovery status via the methods

Scala

def recoveryRunning: Boolean
def recoveryFinished: Boolean

Java

public boolean recoveryRunning();
public boolean recoveryFinished();

Sometimes there is a need for performing additional initialization when the recovery has completed before processing any other message sent to the persistent actor. The persistent actor will receive a special RecoveryCompleted message right after recovery and before any other received messages.

The actor will always receive a RecoveryCompleted message, even if there are no events in the journal and the snapshot store is empty, or if it’s a new persistent actor with a previously unused persistenceId.

If there is a problem with recovering the state of the actor from the journal, onRecoveryFailure is called (logging the error by default) and the actor will be stopped.

Internal stash

The persistent actor has a private stash for internally caching incoming messages during recovery or the persist\persistAll method persisting events. You can still use/inherit from the Stash interface. The internal stash cooperates with the normal stash by hooking into unstashAll method and making sure messages are unstashed properly to the internal stash to maintain ordering guarantees.

You should be careful to not send more messages to a persistent actor than it can keep up with, otherwise the number of stashed messages will grow without bounds. It can be wise to protect against OutOfMemoryError by defining a maximum stash capacity in the mailbox configuration:

akka.actor.default-mailbox.stash-capacity=10000

Note that the stash capacity is per actor. If you have many persistent actors, e.g. when using cluster sharding, you may need to define a small stash capacity to ensure that the total number of stashed messages in the system doesn’t consume too much memory. Additionally, the persistent actor defines three strategies to handle failure when the internal stash capacity is exceeded. The default overflow strategy is the ThrowOverflowExceptionStrategy, which discards the current received message and throws a StashOverflowException, causing actor restart if the default supervision strategy is used. You can override the internalStashOverflowStrategy method to return DiscardToDeadLetterStrategy or ReplyToStrategy for any “individual” persistent actor, or define the “default” for all persistent actors by providing FQCN, which must be a subclass of StashOverflowStrategyConfigurator, in the persistence configuration:

The bounded mailbox should be avoided in the persistent actor, by which the messages come from storage backends may be discarded. You can use bounded stash instead of it.

Relaxed local consistency requirements and high throughput use-cases

If faced with relaxed local consistency requirements and high throughput demands sometimes PersistentActor and its persist may not be enough in terms of consuming incoming Commands at a high rate, because it has to wait until all Events related to a given Command are processed in order to start processing the next Command. While this abstraction is very useful for most cases, sometimes you may be faced with relaxed requirements about consistency – for example you may want to process commands as fast as you can, assuming that the Event will eventually be persisted and handled properly in the background, retroactively reacting to persistence failures if needed.

The persistAsync method provides a tool for implementing high-throughput persistent actors. It will not stash incoming Commands while the Journal is still working on persisting and/or user code is executing event callbacks.

In the below example, the event callbacks may be called “at any time”, even after the next Command has been processed. The ordering between events is still guaranteed (“evt-b-1” will be sent after “evt-a-2”, which will be sent after “evt-a-1” etc.).

In order to implement the pattern known as “command sourcing” call persistAsync(cmd)(...)persistAsync right away on all incoming messages and handle them in the callback.

Warning

The callback will not be invoked if the actor is restarted (or stopped) in between the call to persistAsync and the journal has confirmed the write.

Deferring actions until preceding persist handlers have executed

Sometimes when working with persistAsync or persist you may find that it would be nice to define some actions in terms of ‘‘happens-after the previous persistAsync/persist handlers have been invoked’’. PersistentActor provides utility methods called defer and deferAsync, which work similarly to persist and persistAsync respectively yet do not persist the passed in event. It is recommended to use them for read operations, and actions which do not have corresponding events in your domain model.

Using those methods is very similar to the persist family of methods, yet they do not persist the passed in event. It will be kept in memory and used when invoking the handler.

The callback will not be invoked if the actor is restarted (or stopped) in between the call to defer or deferAsync and the journal has processed and confirmed all preceding writes.

Nested persist calls

It is possible to call persist and persistAsync inside their respective callback blocks and they will properly retain both the thread safety (including the right value of sender()getSender()) as well as stashing guarantees.

In general it is encouraged to create command handlers which do not need to resort to nested event persisting, however there are situations where it may be useful. It is important to understand the ordering of callback execution in those situations, as well as their implication on the stashing behavior (that persist() enforces). In the following example two persist calls are issued, and each of them issues another persist inside its callback:

First the “outer layer” of persist calls is issued and their callbacks are applied. After these have successfully completed, the inner callbacks will be invoked (once the events they are persisting have been confirmed to be persisted by the journal). Only after all these handlers have been successfully invoked will the next command be delivered to the persistent Actor. In other words, the stashing of incoming commands that is guaranteed by initially calling persist() on the outer layer is extended until all nested persist callbacks have been handled.

It is also possible to nest persistAsync calls, using the same pattern:

While it is possible to nest mixed persist and persistAsync with keeping their respective semantics it is not a recommended practice, as it may lead to overly complex nesting.

Warning

While it is possible to nest persist calls within one another, it is not legal call persist from any other Thread than the Actors message processing Thread. For example, it is not legal to call persist from Futures! Doing so will break the guarantees that the persist methods aim to provide. Always call persist and persistAsync from within the Actor’s receive block (or methods synchronously invoked from there).

Failures

If persistence of an event fails, onPersistFailure will be invoked (logging the error by default), and the actor will unconditionally be stopped.

The reason that it cannot resume when persist fails is that it is unknown if the event was actually persisted or not, and therefore it is in an inconsistent state. Restarting on persistent failures will most likely fail anyway since the journal is probably unavailable. It is better to stop the actor and after a back-off timeout start it again. The akka.pattern.BackoffSupervisor actor is provided to support such restarts.

If persistence of an event is rejected before it is stored, e.g. due to serialization error, onPersistRejected will be invoked (logging a warning by default), and the actor continues with next message.

If there is a problem with recovering the state of the actor from the journal when the actor is started, onRecoveryFailure is called (logging the error by default), and the actor will be stopped. Note that failure to load snapshot is also treated like this, but you can disable loading of snapshots if you for example know that serialization format has changed in an incompatible way, see Recovery customization.

Atomic writes

Each event is stored atomically, but it is also possible to store several events atomically by using the persistAll or persistAllAsync method. That means that all events passed to that method are stored or none of them are stored if there is an error.

The recovery of a persistent actor will therefore never be done partially with only a subset of events persisted by persistAll.

Some journals may not support atomic writes of several events and they will then reject the persistAll command, i.e. onPersistRejected is called with an exception (typically UnsupportedOperationException).

Batch writes

In order to optimize throughput when using persistAsync, a persistent actor internally batches events to be stored under high load before writing them to the journal (as a single batch). The batch size is dynamically determined by how many events are emitted during the time of a journal round-trip: after sending a batch to the journal no further batch can be sent before confirmation has been received that the previous batch has been written. Batch writes are never timer-based which keeps latencies at a minimum.

Message deletion

It is possible to delete all messages (journaled by a single persistent actor) up to a specified sequence number; Persistent actors may call the deleteMessages method to this end.

Deleting messages in event sourcing based applications is typically either not used at all, or used in conjunction with snapshotting, i.e. after a snapshot has been successfully stored, a deleteMessages(toSequenceNr) up until the sequence number of the data held by that snapshot can be issued to safely delete the previous events while still having access to the accumulated state during replays - by loading the snapshot.

Warning

If you are using Persistence Query, query results may be missing deleted messages in a journal, depending on how deletions are implemented in the journal plugin. Unless you use a plugin which still shows deleted messages in persistence query results, you have to design your application so that it is not affected by missing messages.

The result of the deleteMessages request is signaled to the persistent actor with a DeleteMessagesSuccess message if the delete was successful or a DeleteMessagesFailure message if it failed.

Message deletion doesn’t affect the highest sequence number of the journal, even if all messages were deleted from it after deleteMessages invocation.

Persistence status handling

Persisting, deleting, and replaying messages can either succeed or fail.

Method

Success

persist / persistAsync

persist handler invoked

onPersistRejected

No automatic actions.

recovery

RecoveryCompleted

deleteMessages

DeleteMessagesSuccess

The most important operations (persist and recovery) have failure handlers modelled as explicit callbacks which the user can override in the PersistentActor. The default implementations of these handlers emit a log message (error for persist/recovery failures, and warning for others), logging the failure cause and information about which message caused the failure.

For critical failures, such as recovery or persisting events failing, the persistent actor will be stopped after the failure handler is invoked. This is because if the underlying journal implementation is signalling persistence failures it is most likely either failing completely or overloaded and restarting right-away and trying to persist the event again will most likely not help the journal recover – as it would likely cause a Thundering herd problem, as many persistent actors would restart and try to persist their events again. Instead, using a BackoffSupervisor (as described in Failures) which implements an exponential-backoff strategy which allows for more breathing room for the journal to recover between restarts of the persistent actor.

Note

Journal implementations may choose to implement a retry mechanism, e.g. such that only after a write fails N number of times a persistence failure is signalled back to the user. In other words, once a journal returns a failure, it is considered fatal by Akka Persistence, and the persistent actor which caused the failure will be stopped.

Check the documentation of the journal implementation you are using for details if/how it is using this technique.

Safely shutting down persistent actors

Special care should be given when shutting down persistent actors from the outside. With normal Actors it is often acceptable to use the special PoisonPill message to signal to an Actor that it should stop itself once it receives this message – in fact this message is handled automatically by Akka, leaving the target actor no way to refuse stopping itself when given a poison pill.

This can be dangerous when used with PersistentActor due to the fact that incoming commands are stashed while the persistent actor is awaiting confirmation from the Journal that events have been written when persist() was used. Since the incoming commands will be drained from the Actor’s mailbox and put into its internal stash while awaiting the confirmation (thus, before calling the persist handlers) the Actor may receive and (auto)handle the PoisonPill before it processes the other messages which have been put into its stash, causing a pre-mature shutdown of the Actor.

Warning

Consider using explicit shut-down messages instead of PoisonPill when working with persistent actors.

The example below highlights how messages arrive in the Actor’s mailbox and how they interact with its internal stashing mechanism when persist() is used. Notice the early stop behavior that occurs when PoisonPill is used:

Replay Filter

There could be cases where event streams are corrupted and multiple writers (i.e. multiple persistent actor instances) journaled different messages with the same sequence number. In such a case, you can configure how you filter replayed messages from multiple writers, upon recovery.

In your configuration, under the akka.persistence.journal.xxx.replay-filter section (where xxx is your journal plugin id), you can select the replay filter mode from one of the following values:

repair-by-discard-old

fail

warn

off

For example, if you configure the replay filter for leveldb plugin, it looks like this:

Snapshots

As you model your domain using actors, you may notice that some actors may be prone to accumulating extremely long event logs and experiencing long recovery times. Sometimes, the right approach may be to split out into a set of shorter lived actors. However, when this is not an option, you can use snapshots to reduce recovery times drastically.

Persistent actors can save snapshots of internal state by calling the saveSnapshot method. If saving of a snapshot succeeds, the persistent actor receives a SaveSnapshotSuccess message, otherwise a SaveSnapshotFailure message

The replayed messages that follow the SnapshotOffer message, if any, are younger than the offered snapshot. They finally recover the persistent actor to its current (i.e. latest) state.

In general, a persistent actor is only offered a snapshot if that persistent actor has previously saved one or more snapshots and at least one of these snapshots matches the SnapshotSelectionCriteria that can be specified for recovery.

If not specified, they default to SnapshotSelectionCriteria.LatestSnapshotSelectionCriteria.latest() which selects the latest (= youngest) snapshot. To disable snapshot-based recovery, applications should use SnapshotSelectionCriteria.NoneSnapshotSelectionCriteria.none(). A recovery where no saved snapshot matches the specified SnapshotSelectionCriteria will replay all journaled messages.

Note

In order to use snapshots, a default snapshot-store (akka.persistence.snapshot-store.plugin) must be configured, or the PersistentActorpersistent actor can pick a snapshot store explicitly by overriding def snapshotPluginId: StringString snapshotPluginId().

Since it is acceptable for some applications to not use any snapshotting, it is legal to not configure a snapshot store. However, Akka will log a warning message when this situation is detected and then continue to operate until an actor tries to store a snapshot, at which point the operation will fail (by replying with an SaveSnapshotFailure for example).

Note that the “persistence mode” of Cluster Sharding makes use of snapshots. If you use that mode, you’ll need to define a snapshot store plugin.

Snapshot deletion

A persistent actor can delete individual snapshots by calling the deleteSnapshot method with the sequence number of when the snapshot was taken.

To bulk-delete a range of snapshots matching SnapshotSelectionCriteria, persistent actors should use the deleteSnapshots method. Depending on the journal used this might be inefficient. It is best practice to do specific deletes with deleteSnapshot or to include a minSequenceNr as well as a maxSequenceNr for the SnapshotSelectionCriteria.

Snapshot status handling

Saving or deleting snapshots can either succeed or fail – this information is reported back to the persistent actor via status messages as illustrated in the following table.

Method

Success

Failure message

saveSnapshot(Any)

SaveSnapshotSuccess

SaveSnapshotFailure

deleteSnapshot(Long)

DeleteSnapshotSuccess

DeleteSnapshotFailure

deleteSnapshots(SnapshotSelectionCriteria)

DeleteSnapshotsSuccess

DeleteSnapshotsFailure

If failure messages are left unhandled by the actor, a default warning log message will be logged for each incoming failure message. No default action is performed on the success messages, however you’re free to handle them e.g. in order to delete an in memory representation of the snapshot, or in the case of failure to attempt save the snapshot again.

Scaling out

In a use case where the number of persistent actors needed are higher than what would fit in the memory of one node or where resilience is important so that if a node crashes the persistent actors are quickly started on a new node and can resume operations Cluster Sharding is an excellent fit to spread persistent actors over a cluster and address them by id.

Akka Persistence is based on the single-writer principle. For a particular persistenceId only one PersistentActor instance should be active at one time. If multiple instances were to persist events at the same time, the events would be interleaved and might not be interpreted correctly on replay. Cluster Sharding ensures that there is only one active entity (PersistentActor) for each id within a data center. Lightbend’s Multi-DC Persistence supports active-active persistent entities across data centers.

At-Least-Once Delivery

To send messages with at-least-once delivery semantics to destinations you can mix-in AtLeastOnceDelivery trait to your PersistentActorextend the AbstractPersistentActorWithAtLeastOnceDelivery class instead of AbstractPersistentActor on the sending side. It takes care of re-sending messages when they have not been confirmed within a configurable timeout.

The state of the sending actor, including which messages have been sent that have not been confirmed by the recipient must be persistent so that it can survive a crash of the sending actor or JVM. The AtLeastOnceDelivery traitAbstractPersistentActorWithAtLeastOnceDelivery class does not persist anything by itself. It is your responsibility to persist the intent that a message is sent and that a confirmation has been received.

Note

At-least-once delivery implies that original message sending order is not always preserved, and the destination may receive duplicate messages. Semantics do not match those of a normal ActorRef send operation:

it is not at-most-once delivery

message order for the same sender–receiver pair is not preserved due to possible resends

after a crash and restart of the destination messages are still delivered to the new actor incarnation

These semantics are similar to what an ActorPath represents (see Actor Lifecycle), therefore you need to supply a path and not a reference when delivering messages. The messages are sent to the path with an actor selection.

Use the deliver method to send a message to a destination. Call the confirmDelivery method when the destination has replied with a confirmation message.

Relationship between deliver and confirmDelivery

To send messages to the destination path, use the deliver method after you have persisted the intent to send the message.

The destination actor must send back a confirmation message. When the sending actor receives this confirmation message you should persist the fact that the message was delivered successfully and then call the confirmDelivery method.

If the persistent actor is not currently recovering, the deliver method will send the message to the destination actor. When recovering, messages will be buffered until they have been confirmed using confirmDelivery. Once recovery has completed, if there are outstanding messages that have not been confirmed (during the message replay), the persistent actor will resend these before sending any other messages.

Deliver requires a deliveryIdToMessage function to pass the provided deliveryId into the message so that the correlation between deliver and confirmDelivery is possible. The deliveryId must do the round trip. Upon receipt of the message, the destination actor will send the samedeliveryId wrapped in a confirmation message back to the sender. The sender will then use it to call confirmDelivery method to complete the delivery routine.

The deliveryId generated by the persistence module is a strictly monotonically increasing sequence number without gaps. The same sequence is used for all destinations of the actor, i.e. when sending to multiple destinations the destinations will see gaps in the sequence. It is not possible to use custom deliveryId. However, you can send a custom correlation identifier in the message to the destination. You must then retain a mapping between the internal deliveryId (passed into the deliveryIdToMessage function) and your custom correlation id (passed into the message). You can do this by storing such mapping in a Map(correlationId -> deliveryId) from which you can retrieve the deliveryId to be passed into the confirmDelivery method once the receiver of your message has replied with your custom correlation id.

The AtLeastOnceDelivery traitAbstractPersistentActorWithAtLeastOnceDelivery class has a state consisting of unconfirmed messages and a sequence number. It does not store this state itself. You must persist events corresponding to the deliver and confirmDelivery invocations from your PersistentActor so that the state can be restored by calling the same methods during the recovery phase of the PersistentActor. Sometimes these events can be derived from other business level events, and sometimes you must create separate events. During recovery, calls to deliver will not send out messages, those will be sent later if no matching confirmDelivery will have been performed.

Support for snapshots is provided by getDeliverySnapshot and setDeliverySnapshot. The AtLeastOnceDeliverySnapshot contains the full delivery state, including unconfirmed messages. If you need a custom snapshot for other parts of the actor state you must also include the AtLeastOnceDeliverySnapshot. It is serialized using protobuf with the ordinary Akka serialization mechanism. It is easiest to include the bytes of the AtLeastOnceDeliverySnapshot as a blob in your custom snapshot.

The interval between redelivery attempts is defined by the redeliverInterval method. The default value can be configured with the akka.persistence.at-least-once-delivery.redeliver-interval configuration key. The method can be overridden by implementation classes to return non-default values.

The maximum number of messages that will be sent at each redelivery burst is defined by the redeliveryBurstLimit method (burst frequency is half of the redelivery interval). If there’s a lot of unconfirmed messages (e.g. if the destination is not available for a long time), this helps to prevent an overwhelming amount of messages to be sent at once. The default value can be configured with the akka.persistence.at-least-once-delivery.redelivery-burst-limit configuration key. The method can be overridden by implementation classes to return non-default values.

After a number of delivery attempts a AtLeastOnceDelivery.UnconfirmedWarning message will be sent to self. The re-sending will still continue, but you can choose to call confirmDelivery to cancel the re-sending. The number of delivery attempts before emitting the warning is defined by the warnAfterNumberOfUnconfirmedAttempts method. The default value can be configured with the akka.persistence.at-least-once-delivery.warn-after-number-of-unconfirmed-attempts configuration key. The method can be overridden by implementation classes to return non-default values.

The AtLeastOnceDelivery traitAbstractPersistentActorWithAtLeastOnceDelivery class holds messages in memory until their successful delivery has been confirmed. The maximum number of unconfirmed messages that the actor is allowed to hold in memory is defined by the maxUnconfirmedMessages method. If this limit is exceed the deliver method will not accept more messages and it will throw AtLeastOnceDelivery.MaxUnconfirmedMessagesExceededException. The default value can be configured with the akka.persistence.at-least-once-delivery.max-unconfirmed-messages configuration key. The method can be overridden by implementation classes to return non-default values.

Event Adapters

In long running projects using event sourcing sometimes the need arises to detach the data model from the domain model completely.

Event Adapters help in situations where:

Version Migrations – existing events stored in Version 1 should be “upcasted” to a new Version 2 representation, and the process of doing so involves actual code, not just changes on the serialization layer. For these scenarios the toJournal function is usually an identity function, however the fromJournal is implemented as v1.Event=>v2.Event, performing the necessary mapping inside the fromJournal method. This technique is sometimes referred to as “upcasting” in other CQRS libraries.

Separating Domain and Data models – thanks to EventAdapters it is possible to completely separate the domain model from the model used to persist data in the Journals. For example one may want to use case classes in the domain model, however persist their protocol-buffer (or any other binary serialization format) counter-parts to the Journal. A simple toJournal:MyModel=>MyDataModel and fromJournal:MyDataModel=>MyModel adapter can be used to implement this feature.

Journal Specialized Data Types – exposing data types understood by the underlying Journal, for example for data stores which understand JSON it is possible to write an EventAdapter toJournal:Any=>JSON such that the Journal can directly store the json instead of serializing the object to its binary representation.

It is possible to bind multiple adapters to one class for recovery, in which case the fromJournal methods of all bound adapters will be applied to a given matching event (in order of definition in the configuration). Since each adapter may return from 0 to n adapted events (called as EventSeq), each adapter can investigate the event and if it should indeed adapt it return the adapted event(s) for it. Other adapters which do not have anything to contribute during this adaptation simply return EventSeq.empty. The adapted events are then delivered in-order to the PersistentActor during replay.

Storage plugins

Storage backends for journals and snapshot stores are pluggable in the Akka persistence extension.

A directory of persistence journal and snapshot store plugins is available at the Akka Community Projects page, see Community plugins

Plugins can be selected either by “default” for all persistent actors, or “individually”, when a persistent actor defines its own set of plugins.

When a persistent actor does NOT override the journalPluginId and snapshotPluginId methods, the persistence extension will use the “default” journal and snapshot-store plugins configured in reference.conf:

However, these entries are provided as empty "", and require explicit user configuration via override in the user application.conf. For an example of a journal plugin which writes messages to LevelDB see Local LevelDB journal. For an example of a snapshot store plugin which writes snapshots as individual files to the local filesystem see Local snapshot store.

Applications can provide their own plugins by implementing a plugin API and activating them by configuration. Plugin development requires the following imports:

Eager initialization of persistence plugin

By default, persistence plugins are started on-demand, as they are used. In some case, however, it might be beneficial to start a certain plugin eagerly. In order to do that, you should first add akka.persistence.Persistence under the akka.extensions key. Then, specify the IDs of plugins you wish to start automatically under akka.persistence.journal.auto-start-journals and akka.persistence.snapshot-store.auto-start-snapshot-stores.

For example, if you want eager initialization for the leveldb journal plugin and the local snapshot store plugin, your configuration should look like this:

The default location of LevelDB files is a directory named journal in the current working directory. This location can be changed by configuration where the specified path can be relative or absolute:

akka.persistence.journal.leveldb.dir = "target/journal"

With this plugin, each actor system runs its own private LevelDB instance.

One peculiarity of LevelDB is that the deletion operation does not remove messages from the journal, but adds a “tombstone” for each deleted message instead. In the case of heavy journal usage, especially one including frequent deletes, this may be an issue as users may find themselves dealing with continuously increasing journal sizes. To this end, LevelDB offers a special journal compaction function that is exposed via the following configuration:

Shared LevelDB journal

A LevelDB instance can also be shared by multiple actor systems (on the same or on different nodes). This, for example, allows persistent actors to failover to a backup node and continue using the shared journal instance from the backup node.

Warning

A shared LevelDB instance is a single point of failure and should therefore only be used for testing purposes. Highly-available, replicated journals are available as Community plugins.

This plugin must be initialized by injecting the (remote) SharedLeveldbStore actor reference. Injection is done by calling the SharedLeveldbJournal.setStore method with the actor reference as argument.

Internal journal commands (sent by persistent actors) are buffered until injection completes. Injection is idempotent i.e. only the first injection is used.

Local snapshot store

The local snapshot store plugin config entry is akka.persistence.snapshot-store.local. It writes snapshot files to the local filesystem. Enable this plugin by defining config property:

# Path to the snapshot store plugin to be used
akka.persistence.snapshot-store.plugin = "akka.persistence.snapshot-store.local"

The default storage location is a directory named snapshots in the current working directory. This can be changed by configuration where the specified path can be relative or absolute:

akka.persistence.snapshot-store.local.dir = "target/snapshots"

Note that it is not mandatory to specify a snapshot store plugin. If you don’t use snapshots you don’t have to configure it.

Persistence Plugin Proxy

A persistence plugin proxy allows sharing of journals and snapshot stores across multiple actor systems (on the same or on different nodes). This, for example, allows persistent actors to failover to a backup node and continue using the shared journal instance from the backup node. The proxy works by forwarding all the journal/snapshot store messages to a single, shared, persistence plugin instance, and therefore supports any use case supported by the proxied plugin.

Warning

A shared journal/snapshot store is a single point of failure and should therefore only be used for testing purposes. Highly-available, replicated persistence plugins are available as Community plugins.

The journal and snapshot store proxies are controlled via the akka.persistence.journal.proxy and akka.persistence.snapshot-store.proxy configuration entries, respectively. Set the target-journal-plugin or target-snapshot-store-plugin keys to the underlying plugin you wish to use (for example: akka.persistence.journal.leveldb). The start-target-journal and start-target-snapshot-store keys should be set to on in exactly one actor system - this is the system that will instantiate the shared persistence plugin. Next, the proxy needs to be told how to find the shared plugin. This can be done by setting the target-journal-address and target-snapshot-store-address configuration keys, or programmatically by calling the PersistencePluginProxy.setTargetLocation method.

Note

Akka starts extensions lazily when they are required, and this includes the proxy. This means that in order for the proxy to work, the persistence plugin on the target node must be instantiated. This can be done by instantiating the PersistencePluginProxyExtensionextension, or by calling the PersistencePluginProxy.start method.

Note

The proxied persistence plugin can (and should) be configured using its original configuration keys.

Custom serialization

Serialization of snapshots and payloads of Persistent messages is configurable with Akka’s Serialization infrastructure. For example, if an application wants to serialize

Testing

When running tests with LevelDB default settings in sbt, make sure to set fork := true in your sbt project. Otherwise, you’ll see an UnsatisfiedLinkError. Alternatively, you can switch to a LevelDB Java port by setting

akka.persistence.journal.leveldb.native = off

or

akka.persistence.journal.leveldb-shared.store.native = off

in your Akka configuration. The LevelDB Java port is for testing purposes only.

Also note that for the LevelDB Java port, you will need the following dependencies:

It is not possible to test persistence provided classes (i.e. PersistentActor and AtLeastOnceDelivery) using TestActorRef due to its synchronous nature. These traits need to be able to perform asynchronous tasks in the background in order to handle internal persistence related events.

Note that journalPluginId and snapshotPluginId must refer to properly configured reference.conf plugin entries with a standard class property as well as settings which are specific for those plugins, i.e.:

Give persistence plugin configurations at runtime

By default, a persistent actor will use the configuration loaded at ActorSystem creation time to create journal and snapshot store plugins.

When the persistent actor overrides the journalPluginConfig and snapshotPluginConfig methods, the actor will use the declared Config objects with a fallback on the default configuration. It allows a dynamic configuration of the journal and the snapshot store at runtime: